Despite its rapid advance, the DL paradigm faces several challenges. DL
networks are mathematically intractable, vulnerable to adversarial
attacks [1]}, and demand heavy supervision. Efforts
have been made in explaining the behavior of DL networks with certain
success, e.g., [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Adversarial training has
been developed to provide a tradeoff between robustness and accuracy
[8]}. Self-supervised [9]} and
semi-supervised learning [10]}, [11]} have
been explored to reduce the supervision burden.
